{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "481f59fc",
   "metadata": {},
   "source": [
    "# MobileNetV3 的核心创新点\n",
    "\n",
    "## 1. 候选块（Candidate Block）与内积激活函数\n",
    "- 使用**候选块**替代传统固定卷积单元，通过组合不同操作（卷积层、批标准化层、激活函数）提升灵活性\n",
    "- 引入**内积激活函数**（如h-swish），在保持计算效率的同时增强非线性表达能力\n",
    "\n",
    "<img src=\"resources/mobilenetv3_hswish.png\" alt=\"drawing\" width=\"40%\"/>\n",
    "\n",
    "## 2. 轻量化模块优化与 SE 注意力模块\n",
    "- 在 MobileNetV2 的深度可分离卷积和残差结构基础上，新增**SE 模块**（Squeeze-and-Excitation），通过通道注意力机制动态调整特征权重，显著提升模型性能\n",
    "\n",
    "## 3. 网络架构搜索（NAS）与 NetAdapt 联合优化\n",
    "- 采用**互补搜索技术**：模块级搜索通过资源受限的 NAS 实现，局部搜索由 NetAdapt 完成，最终得到更高效的网络结构\n",
    "- 通过 NAS 重新设计耗时层结构，进一步优化网络深度和宽度\n",
    "\n",
    "## 4. 解码器改进（LR-ASPP）\n",
    "- 针对图像分割任务，提出轻量级解码结构 **LR-ASPP**（Lite Reduced Atrous Spatial Pyramid Pooling），在减少计算量的同时保持多尺度特征融合能力\n",
    "\n",
    "## 5. Bottleneck 结构升级\n",
    "- 改进传统 bottleneck 结构，通过调整网络深度和宽度（如增加中间层通道数），在相同计算量下提升模型表达能力\n",
    "\n",
    "---\n",
    "\n",
    "这些创新点共同推动了 MobileNetV3 在移动端设备上的性能突破，具体可参考其论文和开源实现进一步验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2a0923c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Use device:  cuda\n"
     ]
    }
   ],
   "source": [
    "# 自动重新加载外部module，使得修改代码之后无需重新import\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from hdd.device.utils import get_device\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# 设置训练数据的路径\n",
    "DATA_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置TensorBoard的路径\n",
    "TENSORBOARD_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置预训练模型参数路径\n",
    "TORCH_HUB_PATH = \"~/workspace/hands-dirty-on-dl/pretrained_models\"\n",
    "torch.hub.set_dir(TORCH_HUB_PATH)\n",
    "# 挑选最合适的训练设备\n",
    "DEVICE = get_device([\"cuda\", \"cpu\"])\n",
    "print(\"Use device: \", DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fc08b81e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hdd.dataset.imagenette_in_memory import ImagenetteInMemory\n",
    "\n",
    "train_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"train\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=None,\n",
    ")\n",
    "val_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"val\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c8c6fda1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------mobilenet_v3_small------------------------\n",
      "#Parameter: 1528106\n",
      "Epoch: 1/150 Train Loss: 2.1417 Accuracy: 0.2393 Time: 4.89135  | Val Loss: 2.3124 Accuracy: 0.1006\n",
      "Epoch: 2/150 Train Loss: 1.9063 Accuracy: 0.3785 Time: 4.85221  | Val Loss: 2.3260 Accuracy: 0.0986\n",
      "Epoch: 3/150 Train Loss: 1.7884 Accuracy: 0.4383 Time: 4.94501  | Val Loss: 1.9843 Accuracy: 0.3623\n",
      "Epoch: 4/150 Train Loss: 1.6715 Accuracy: 0.4948 Time: 4.92335  | Val Loss: 1.4127 Accuracy: 0.6252\n",
      "Epoch: 5/150 Train Loss: 1.5984 Accuracy: 0.5293 Time: 4.86367  | Val Loss: 1.5025 Accuracy: 0.5860\n",
      "Epoch: 6/150 Train Loss: 1.5115 Accuracy: 0.5651 Time: 4.84847  | Val Loss: 1.4318 Accuracy: 0.6288\n",
      "Epoch: 7/150 Train Loss: 1.4641 Accuracy: 0.5886 Time: 4.86499  | Val Loss: 1.3167 Accuracy: 0.6571\n",
      "Epoch: 8/150 Train Loss: 1.4288 Accuracy: 0.6038 Time: 4.88861  | Val Loss: 1.9676 Accuracy: 0.4721\n",
      "Epoch: 9/150 Train Loss: 1.3767 Accuracy: 0.6276 Time: 4.86431  | Val Loss: 1.3327 Accuracy: 0.6662\n",
      "Epoch: 10/150 Train Loss: 1.3299 Accuracy: 0.6492 Time: 4.87760  | Val Loss: 1.4137 Accuracy: 0.6285\n",
      "Epoch: 11/150 Train Loss: 1.3077 Accuracy: 0.6587 Time: 4.90696  | Val Loss: 1.1873 Accuracy: 0.7266\n",
      "Epoch: 12/150 Train Loss: 1.2867 Accuracy: 0.6692 Time: 4.90772  | Val Loss: 1.1260 Accuracy: 0.7409\n",
      "Epoch: 13/150 Train Loss: 1.2575 Accuracy: 0.6788 Time: 4.87351  | Val Loss: 1.1665 Accuracy: 0.7215\n",
      "Epoch: 14/150 Train Loss: 1.2433 Accuracy: 0.6881 Time: 4.89755  | Val Loss: 1.1835 Accuracy: 0.7190\n",
      "Epoch: 15/150 Train Loss: 1.2196 Accuracy: 0.6971 Time: 4.93205  | Val Loss: 1.1929 Accuracy: 0.7106\n",
      "Epoch: 16/150 Train Loss: 1.1947 Accuracy: 0.7112 Time: 5.00857  | Val Loss: 1.0996 Accuracy: 0.7638\n",
      "Epoch: 17/150 Train Loss: 1.1804 Accuracy: 0.7153 Time: 4.91668  | Val Loss: 1.0364 Accuracy: 0.7738\n",
      "Epoch: 18/150 Train Loss: 1.1848 Accuracy: 0.7130 Time: 4.93878  | Val Loss: 1.1036 Accuracy: 0.7552\n",
      "Epoch: 19/150 Train Loss: 1.1448 Accuracy: 0.7309 Time: 4.93858  | Val Loss: 1.0448 Accuracy: 0.7732\n",
      "Epoch: 20/150 Train Loss: 1.1471 Accuracy: 0.7321 Time: 4.99242  | Val Loss: 0.9916 Accuracy: 0.8010\n",
      "Epoch: 21/150 Train Loss: 1.1237 Accuracy: 0.7389 Time: 4.87246  | Val Loss: 1.0144 Accuracy: 0.7929\n",
      "Epoch: 22/150 Train Loss: 1.1170 Accuracy: 0.7418 Time: 4.91179  | Val Loss: 1.0471 Accuracy: 0.7786\n",
      "Epoch: 23/150 Train Loss: 1.1089 Accuracy: 0.7499 Time: 4.86545  | Val Loss: 0.9990 Accuracy: 0.7964\n",
      "Epoch: 24/150 Train Loss: 1.0840 Accuracy: 0.7582 Time: 4.87084  | Val Loss: 0.9323 Accuracy: 0.8206\n",
      "Epoch: 25/150 Train Loss: 1.0831 Accuracy: 0.7588 Time: 4.92822  | Val Loss: 0.9341 Accuracy: 0.8321\n",
      "Epoch: 26/150 Train Loss: 1.0601 Accuracy: 0.7700 Time: 4.84201  | Val Loss: 0.9998 Accuracy: 0.8008\n",
      "Epoch: 27/150 Train Loss: 1.0546 Accuracy: 0.7728 Time: 4.95264  | Val Loss: 0.9133 Accuracy: 0.8329\n",
      "Epoch: 28/150 Train Loss: 1.0348 Accuracy: 0.7821 Time: 4.95649  | Val Loss: 0.9209 Accuracy: 0.8290\n",
      "Epoch: 29/150 Train Loss: 1.0252 Accuracy: 0.7858 Time: 4.89035  | Val Loss: 0.9171 Accuracy: 0.8303\n",
      "Epoch: 30/150 Train Loss: 1.0267 Accuracy: 0.7854 Time: 4.86383  | Val Loss: 0.8877 Accuracy: 0.8433\n",
      "Epoch: 31/150 Train Loss: 1.0208 Accuracy: 0.7835 Time: 4.92363  | Val Loss: 0.8962 Accuracy: 0.8448\n",
      "Epoch: 32/150 Train Loss: 1.0189 Accuracy: 0.7853 Time: 4.84414  | Val Loss: 0.9065 Accuracy: 0.8344\n",
      "Epoch: 33/150 Train Loss: 1.0016 Accuracy: 0.7923 Time: 4.92227  | Val Loss: 0.9487 Accuracy: 0.8176\n",
      "Epoch: 34/150 Train Loss: 0.9865 Accuracy: 0.7981 Time: 4.88631  | Val Loss: 0.8854 Accuracy: 0.8454\n",
      "Epoch: 35/150 Train Loss: 0.9912 Accuracy: 0.7947 Time: 4.93417  | Val Loss: 0.8666 Accuracy: 0.8540\n",
      "Epoch: 36/150 Train Loss: 0.9882 Accuracy: 0.7990 Time: 4.91718  | Val Loss: 0.8870 Accuracy: 0.8443\n",
      "Epoch: 37/150 Train Loss: 0.9828 Accuracy: 0.7979 Time: 4.94087  | Val Loss: 0.9136 Accuracy: 0.8349\n",
      "Epoch: 38/150 Train Loss: 0.9764 Accuracy: 0.8017 Time: 4.88309  | Val Loss: 0.9117 Accuracy: 0.8415\n",
      "Epoch: 39/150 Train Loss: 0.9661 Accuracy: 0.8039 Time: 4.96827  | Val Loss: 0.8498 Accuracy: 0.8558\n",
      "Epoch: 40/150 Train Loss: 0.9678 Accuracy: 0.8077 Time: 4.91769  | Val Loss: 0.8766 Accuracy: 0.8425\n",
      "Epoch: 41/150 Train Loss: 0.9479 Accuracy: 0.8174 Time: 4.99601  | Val Loss: 0.8794 Accuracy: 0.8441\n",
      "Epoch: 42/150 Train Loss: 0.9355 Accuracy: 0.8240 Time: 4.97495  | Val Loss: 0.8856 Accuracy: 0.8382\n",
      "Epoch: 43/150 Train Loss: 0.9331 Accuracy: 0.8209 Time: 4.87227  | Val Loss: 0.9068 Accuracy: 0.8382\n",
      "Epoch: 44/150 Train Loss: 0.9405 Accuracy: 0.8176 Time: 4.94332  | Val Loss: 0.8320 Accuracy: 0.8690\n",
      "Epoch: 45/150 Train Loss: 0.9240 Accuracy: 0.8238 Time: 4.86008  | Val Loss: 0.8689 Accuracy: 0.8515\n",
      "Epoch: 46/150 Train Loss: 0.9209 Accuracy: 0.8257 Time: 4.91051  | Val Loss: 0.8234 Accuracy: 0.8690\n",
      "Epoch: 47/150 Train Loss: 0.9284 Accuracy: 0.8243 Time: 4.86673  | Val Loss: 0.8455 Accuracy: 0.8606\n",
      "Epoch: 48/150 Train Loss: 0.9145 Accuracy: 0.8250 Time: 5.02755  | Val Loss: 0.9554 Accuracy: 0.8189\n",
      "Epoch: 49/150 Train Loss: 0.9034 Accuracy: 0.8344 Time: 4.92041  | Val Loss: 0.8574 Accuracy: 0.8489\n",
      "Epoch: 50/150 Train Loss: 0.9066 Accuracy: 0.8328 Time: 4.88657  | Val Loss: 0.8311 Accuracy: 0.8670\n",
      "Epoch: 51/150 Train Loss: 0.8993 Accuracy: 0.8364 Time: 4.90578  | Val Loss: 0.8711 Accuracy: 0.8550\n",
      "Epoch: 52/150 Train Loss: 0.8883 Accuracy: 0.8397 Time: 4.86131  | Val Loss: 0.8231 Accuracy: 0.8701\n",
      "Epoch: 53/150 Train Loss: 0.8815 Accuracy: 0.8440 Time: 4.98085  | Val Loss: 0.8824 Accuracy: 0.8443\n",
      "Epoch: 54/150 Train Loss: 0.8815 Accuracy: 0.8423 Time: 4.91618  | Val Loss: 0.8161 Accuracy: 0.8744\n",
      "Epoch: 55/150 Train Loss: 0.8867 Accuracy: 0.8422 Time: 4.85911  | Val Loss: 0.9046 Accuracy: 0.8336\n",
      "Epoch: 56/150 Train Loss: 0.8774 Accuracy: 0.8451 Time: 4.95326  | Val Loss: 0.8329 Accuracy: 0.8706\n",
      "Epoch: 57/150 Train Loss: 0.8679 Accuracy: 0.8507 Time: 4.96714  | Val Loss: 0.8262 Accuracy: 0.8736\n",
      "Epoch: 58/150 Train Loss: 0.8681 Accuracy: 0.8462 Time: 4.91880  | Val Loss: 0.8063 Accuracy: 0.8703\n",
      "Epoch: 59/150 Train Loss: 0.8497 Accuracy: 0.8529 Time: 4.92346  | Val Loss: 0.8560 Accuracy: 0.8596\n",
      "Epoch: 60/150 Train Loss: 0.8666 Accuracy: 0.8527 Time: 4.87018  | Val Loss: 0.8399 Accuracy: 0.8622\n",
      "Epoch: 61/150 Train Loss: 0.8569 Accuracy: 0.8520 Time: 4.90531  | Val Loss: 0.8534 Accuracy: 0.8601\n",
      "Epoch: 62/150 Train Loss: 0.8497 Accuracy: 0.8563 Time: 4.91173  | Val Loss: 0.7893 Accuracy: 0.8841\n",
      "Epoch: 63/150 Train Loss: 0.8423 Accuracy: 0.8600 Time: 4.87313  | Val Loss: 0.8031 Accuracy: 0.8777\n",
      "Epoch: 64/150 Train Loss: 0.8411 Accuracy: 0.8592 Time: 4.90972  | Val Loss: 0.8270 Accuracy: 0.8670\n",
      "Epoch: 65/150 Train Loss: 0.8365 Accuracy: 0.8627 Time: 4.89712  | Val Loss: 0.8035 Accuracy: 0.8767\n",
      "Epoch: 66/150 Train Loss: 0.8355 Accuracy: 0.8607 Time: 4.89040  | Val Loss: 0.8699 Accuracy: 0.8512\n",
      "Epoch: 67/150 Train Loss: 0.8265 Accuracy: 0.8655 Time: 4.89419  | Val Loss: 0.8313 Accuracy: 0.8703\n",
      "Epoch: 68/150 Train Loss: 0.8235 Accuracy: 0.8669 Time: 4.94451  | Val Loss: 0.7832 Accuracy: 0.8854\n",
      "Epoch: 69/150 Train Loss: 0.8225 Accuracy: 0.8662 Time: 4.90905  | Val Loss: 0.8466 Accuracy: 0.8624\n",
      "Epoch: 70/150 Train Loss: 0.8219 Accuracy: 0.8670 Time: 4.94085  | Val Loss: 0.7915 Accuracy: 0.8841\n",
      "Epoch: 71/150 Train Loss: 0.8116 Accuracy: 0.8734 Time: 5.01490  | Val Loss: 0.7774 Accuracy: 0.8899\n",
      "Epoch: 72/150 Train Loss: 0.8129 Accuracy: 0.8739 Time: 4.95924  | Val Loss: 0.7842 Accuracy: 0.8810\n",
      "Epoch: 73/150 Train Loss: 0.8112 Accuracy: 0.8707 Time: 4.89284  | Val Loss: 0.8000 Accuracy: 0.8848\n",
      "Epoch: 74/150 Train Loss: 0.8007 Accuracy: 0.8778 Time: 4.90269  | Val Loss: 0.7779 Accuracy: 0.8917\n",
      "Epoch: 75/150 Train Loss: 0.8005 Accuracy: 0.8760 Time: 4.92313  | Val Loss: 0.8257 Accuracy: 0.8736\n",
      "Epoch: 76/150 Train Loss: 0.7932 Accuracy: 0.8809 Time: 4.89280  | Val Loss: 0.7967 Accuracy: 0.8836\n",
      "Epoch: 77/150 Train Loss: 0.8062 Accuracy: 0.8749 Time: 4.92096  | Val Loss: 0.8150 Accuracy: 0.8754\n",
      "Epoch: 78/150 Train Loss: 0.7858 Accuracy: 0.8840 Time: 4.90093  | Val Loss: 0.8446 Accuracy: 0.8561\n",
      "Epoch: 79/150 Train Loss: 0.7835 Accuracy: 0.8821 Time: 4.90348  | Val Loss: 0.7861 Accuracy: 0.8869\n",
      "Epoch: 80/150 Train Loss: 0.7787 Accuracy: 0.8851 Time: 4.88666  | Val Loss: 0.7865 Accuracy: 0.8823\n",
      "Epoch: 81/150 Train Loss: 0.7797 Accuracy: 0.8850 Time: 4.90601  | Val Loss: 0.7859 Accuracy: 0.8851\n",
      "Epoch: 82/150 Train Loss: 0.7772 Accuracy: 0.8850 Time: 4.95803  | Val Loss: 0.8006 Accuracy: 0.8787\n",
      "Epoch: 83/150 Train Loss: 0.7673 Accuracy: 0.8909 Time: 4.93420  | Val Loss: 0.7866 Accuracy: 0.8859\n",
      "Epoch: 84/150 Train Loss: 0.7736 Accuracy: 0.8852 Time: 4.93508  | Val Loss: 0.7771 Accuracy: 0.8910\n",
      "Epoch: 85/150 Train Loss: 0.7669 Accuracy: 0.8924 Time: 4.91346  | Val Loss: 0.7888 Accuracy: 0.8815\n",
      "Epoch: 86/150 Train Loss: 0.7589 Accuracy: 0.8954 Time: 4.85689  | Val Loss: 0.7840 Accuracy: 0.8884\n",
      "Epoch: 87/150 Train Loss: 0.7559 Accuracy: 0.8966 Time: 5.00871  | Val Loss: 0.7729 Accuracy: 0.8922\n",
      "Epoch: 88/150 Train Loss: 0.7581 Accuracy: 0.8953 Time: 5.02288  | Val Loss: 0.7720 Accuracy: 0.8950\n",
      "Epoch: 89/150 Train Loss: 0.7451 Accuracy: 0.9008 Time: 4.89154  | Val Loss: 0.7817 Accuracy: 0.8915\n",
      "Epoch: 90/150 Train Loss: 0.7495 Accuracy: 0.8982 Time: 4.95508  | Val Loss: 0.7836 Accuracy: 0.8892\n",
      "Epoch: 91/150 Train Loss: 0.7519 Accuracy: 0.8971 Time: 4.89048  | Val Loss: 0.7812 Accuracy: 0.8884\n",
      "Epoch: 92/150 Train Loss: 0.7448 Accuracy: 0.9006 Time: 4.96342  | Val Loss: 0.7644 Accuracy: 0.8958\n",
      "Epoch: 93/150 Train Loss: 0.7449 Accuracy: 0.8984 Time: 4.88278  | Val Loss: 0.7701 Accuracy: 0.8953\n",
      "Epoch: 94/150 Train Loss: 0.7475 Accuracy: 0.8999 Time: 4.94942  | Val Loss: 0.7632 Accuracy: 0.8950\n",
      "Epoch: 95/150 Train Loss: 0.7303 Accuracy: 0.9064 Time: 5.00266  | Val Loss: 0.7770 Accuracy: 0.8917\n",
      "Epoch: 96/150 Train Loss: 0.7447 Accuracy: 0.9006 Time: 4.88957  | Val Loss: 0.7832 Accuracy: 0.8907\n",
      "Epoch: 97/150 Train Loss: 0.7419 Accuracy: 0.9002 Time: 4.97540  | Val Loss: 0.7602 Accuracy: 0.8945\n",
      "Epoch: 98/150 Train Loss: 0.7373 Accuracy: 0.9048 Time: 4.87645  | Val Loss: 0.7705 Accuracy: 0.8935\n",
      "Epoch: 99/150 Train Loss: 0.7241 Accuracy: 0.9101 Time: 4.98511  | Val Loss: 0.7635 Accuracy: 0.8953\n",
      "Epoch: 100/150 Train Loss: 0.7245 Accuracy: 0.9091 Time: 4.91465  | Val Loss: 0.7852 Accuracy: 0.8869\n",
      "Epoch: 101/150 Train Loss: 0.7257 Accuracy: 0.9080 Time: 4.95985  | Val Loss: 0.7652 Accuracy: 0.8940\n",
      "Epoch: 102/150 Train Loss: 0.7175 Accuracy: 0.9119 Time: 4.93345  | Val Loss: 0.7612 Accuracy: 0.8971\n",
      "Epoch: 103/150 Train Loss: 0.7233 Accuracy: 0.9092 Time: 4.88908  | Val Loss: 0.7810 Accuracy: 0.8907\n",
      "Epoch: 104/150 Train Loss: 0.7263 Accuracy: 0.9076 Time: 4.89965  | Val Loss: 0.7644 Accuracy: 0.8963\n",
      "Epoch: 105/150 Train Loss: 0.7203 Accuracy: 0.9094 Time: 4.89294  | Val Loss: 0.7595 Accuracy: 0.8976\n",
      "Epoch: 106/150 Train Loss: 0.7202 Accuracy: 0.9110 Time: 4.87301  | Val Loss: 0.7525 Accuracy: 0.8983\n",
      "Epoch: 107/150 Train Loss: 0.7220 Accuracy: 0.9086 Time: 4.97414  | Val Loss: 0.7575 Accuracy: 0.8991\n",
      "Epoch: 108/150 Train Loss: 0.7086 Accuracy: 0.9152 Time: 5.01011  | Val Loss: 0.7553 Accuracy: 0.8991\n",
      "Epoch: 109/150 Train Loss: 0.7054 Accuracy: 0.9175 Time: 4.90087  | Val Loss: 0.7567 Accuracy: 0.8986\n",
      "Epoch: 110/150 Train Loss: 0.7116 Accuracy: 0.9134 Time: 4.93502  | Val Loss: 0.7777 Accuracy: 0.8894\n",
      "Epoch: 111/150 Train Loss: 0.7072 Accuracy: 0.9136 Time: 4.89010  | Val Loss: 0.7612 Accuracy: 0.8945\n",
      "Epoch: 112/150 Train Loss: 0.7042 Accuracy: 0.9184 Time: 4.91362  | Val Loss: 0.7603 Accuracy: 0.8986\n",
      "Epoch: 113/150 Train Loss: 0.6978 Accuracy: 0.9203 Time: 5.02592  | Val Loss: 0.7610 Accuracy: 0.8976\n",
      "Epoch: 114/150 Train Loss: 0.7042 Accuracy: 0.9195 Time: 5.01454  | Val Loss: 0.7575 Accuracy: 0.8961\n",
      "Epoch: 115/150 Train Loss: 0.6998 Accuracy: 0.9199 Time: 4.88863  | Val Loss: 0.7495 Accuracy: 0.9024\n",
      "Epoch: 116/150 Train Loss: 0.6912 Accuracy: 0.9223 Time: 4.90646  | Val Loss: 0.7625 Accuracy: 0.8991\n",
      "Epoch: 117/150 Train Loss: 0.6951 Accuracy: 0.9205 Time: 4.93839  | Val Loss: 0.7639 Accuracy: 0.9001\n",
      "Epoch: 118/150 Train Loss: 0.6871 Accuracy: 0.9254 Time: 4.84386  | Val Loss: 0.7627 Accuracy: 0.8989\n",
      "Epoch: 119/150 Train Loss: 0.6903 Accuracy: 0.9241 Time: 4.88032  | Val Loss: 0.7568 Accuracy: 0.9027\n",
      "Epoch: 120/150 Train Loss: 0.6948 Accuracy: 0.9229 Time: 4.99681  | Val Loss: 0.7563 Accuracy: 0.8986\n",
      "Epoch: 121/150 Train Loss: 0.6867 Accuracy: 0.9238 Time: 4.95150  | Val Loss: 0.7503 Accuracy: 0.9042\n",
      "Epoch: 122/150 Train Loss: 0.6931 Accuracy: 0.9228 Time: 5.01062  | Val Loss: 0.7488 Accuracy: 0.9034\n",
      "Epoch: 123/150 Train Loss: 0.6902 Accuracy: 0.9240 Time: 4.99276  | Val Loss: 0.7545 Accuracy: 0.9011\n",
      "Epoch: 124/150 Train Loss: 0.6985 Accuracy: 0.9205 Time: 4.99259  | Val Loss: 0.7531 Accuracy: 0.9004\n",
      "Epoch: 125/150 Train Loss: 0.6842 Accuracy: 0.9255 Time: 4.94972  | Val Loss: 0.7574 Accuracy: 0.9022\n",
      "Epoch: 126/150 Train Loss: 0.6859 Accuracy: 0.9253 Time: 4.90472  | Val Loss: 0.7519 Accuracy: 0.9027\n",
      "Epoch: 127/150 Train Loss: 0.6826 Accuracy: 0.9269 Time: 4.99404  | Val Loss: 0.7517 Accuracy: 0.9014\n",
      "Epoch: 128/150 Train Loss: 0.6833 Accuracy: 0.9241 Time: 4.95152  | Val Loss: 0.7536 Accuracy: 0.9014\n",
      "Epoch: 129/150 Train Loss: 0.6809 Accuracy: 0.9255 Time: 4.94011  | Val Loss: 0.7529 Accuracy: 0.9017\n",
      "Epoch: 130/150 Train Loss: 0.6852 Accuracy: 0.9252 Time: 4.93462  | Val Loss: 0.7492 Accuracy: 0.9032\n",
      "Epoch: 131/150 Train Loss: 0.6928 Accuracy: 0.9229 Time: 4.89533  | Val Loss: 0.7513 Accuracy: 0.9019\n",
      "Epoch: 132/150 Train Loss: 0.6808 Accuracy: 0.9263 Time: 4.96878  | Val Loss: 0.7472 Accuracy: 0.9014\n",
      "Epoch: 133/150 Train Loss: 0.6882 Accuracy: 0.9251 Time: 4.95393  | Val Loss: 0.7501 Accuracy: 0.9032\n",
      "Epoch: 134/150 Train Loss: 0.6774 Accuracy: 0.9295 Time: 4.90678  | Val Loss: 0.7475 Accuracy: 0.9022\n",
      "Epoch: 135/150 Train Loss: 0.6729 Accuracy: 0.9332 Time: 4.91209  | Val Loss: 0.7485 Accuracy: 0.9019\n",
      "Epoch: 136/150 Train Loss: 0.6815 Accuracy: 0.9258 Time: 5.03155  | Val Loss: 0.7468 Accuracy: 0.9009\n",
      "Epoch: 137/150 Train Loss: 0.6729 Accuracy: 0.9316 Time: 5.04611  | Val Loss: 0.7503 Accuracy: 0.9024\n",
      "Epoch: 138/150 Train Loss: 0.6657 Accuracy: 0.9340 Time: 4.91053  | Val Loss: 0.7519 Accuracy: 0.9006\n",
      "Epoch: 139/150 Train Loss: 0.6864 Accuracy: 0.9229 Time: 4.90940  | Val Loss: 0.7516 Accuracy: 0.9009\n",
      "Epoch: 140/150 Train Loss: 0.6812 Accuracy: 0.9276 Time: 4.89008  | Val Loss: 0.7510 Accuracy: 0.9004\n",
      "Epoch: 141/150 Train Loss: 0.6753 Accuracy: 0.9309 Time: 4.91076  | Val Loss: 0.7500 Accuracy: 0.9006\n",
      "Epoch: 142/150 Train Loss: 0.6863 Accuracy: 0.9248 Time: 4.94795  | Val Loss: 0.7474 Accuracy: 0.9019\n",
      "Epoch: 143/150 Train Loss: 0.6738 Accuracy: 0.9305 Time: 4.93696  | Val Loss: 0.7469 Accuracy: 0.9014\n",
      "Epoch: 144/150 Train Loss: 0.6714 Accuracy: 0.9318 Time: 4.96134  | Val Loss: 0.7480 Accuracy: 0.9029\n",
      "Epoch: 145/150 Train Loss: 0.6716 Accuracy: 0.9324 Time: 4.93241  | Val Loss: 0.7468 Accuracy: 0.9019\n",
      "Epoch: 146/150 Train Loss: 0.6712 Accuracy: 0.9314 Time: 4.89193  | Val Loss: 0.7473 Accuracy: 0.9017\n",
      "Epoch: 147/150 Train Loss: 0.6722 Accuracy: 0.9289 Time: 4.97620  | Val Loss: 0.7478 Accuracy: 0.9004\n",
      "Epoch: 148/150 Train Loss: 0.6718 Accuracy: 0.9310 Time: 4.88963  | Val Loss: 0.7486 Accuracy: 0.9011\n",
      "Epoch: 149/150 Train Loss: 0.6756 Accuracy: 0.9323 Time: 4.92099  | Val Loss: 0.7486 Accuracy: 0.9017\n",
      "Epoch: 150/150 Train Loss: 0.6750 Accuracy: 0.9309 Time: 4.93811  | Val Loss: 0.7484 Accuracy: 0.9032\n",
      "-----------------------mobilenet_v3_large------------------------\n",
      "#Parameter: 4214842\n",
      "Epoch: 1/150 Train Loss: 2.1449 Accuracy: 0.2333 Time: 7.22529  | Val Loss: 2.3630 Accuracy: 0.1006\n",
      "Epoch: 2/150 Train Loss: 1.9267 Accuracy: 0.3439 Time: 6.99166  | Val Loss: 1.9298 Accuracy: 0.3654\n",
      "Epoch: 3/150 Train Loss: 1.7871 Accuracy: 0.4326 Time: 6.95430  | Val Loss: 2.3386 Accuracy: 0.3569\n",
      "Epoch: 4/150 Train Loss: 1.6703 Accuracy: 0.4910 Time: 6.94036  | Val Loss: 2.3732 Accuracy: 0.4071\n",
      "Epoch: 5/150 Train Loss: 1.5990 Accuracy: 0.5251 Time: 6.99559  | Val Loss: 1.5208 Accuracy: 0.5972\n",
      "Epoch: 6/150 Train Loss: 1.5249 Accuracy: 0.5569 Time: 6.99267  | Val Loss: 1.6559 Accuracy: 0.5580\n",
      "Epoch: 7/150 Train Loss: 1.4710 Accuracy: 0.5854 Time: 7.00590  | Val Loss: 1.4222 Accuracy: 0.6163\n",
      "Epoch: 8/150 Train Loss: 1.4146 Accuracy: 0.6161 Time: 7.02311  | Val Loss: 1.4401 Accuracy: 0.6209\n",
      "Epoch: 9/150 Train Loss: 1.3490 Accuracy: 0.6436 Time: 7.05738  | Val Loss: 1.2961 Accuracy: 0.6655\n",
      "Epoch: 10/150 Train Loss: 1.3289 Accuracy: 0.6518 Time: 7.00254  | Val Loss: 1.1614 Accuracy: 0.7468\n",
      "Epoch: 11/150 Train Loss: 1.3032 Accuracy: 0.6621 Time: 7.02666  | Val Loss: 1.1654 Accuracy: 0.7241\n",
      "Epoch: 12/150 Train Loss: 1.2627 Accuracy: 0.6793 Time: 7.00331  | Val Loss: 1.1652 Accuracy: 0.7299\n",
      "Epoch: 13/150 Train Loss: 1.2314 Accuracy: 0.6929 Time: 7.00015  | Val Loss: 1.1024 Accuracy: 0.7564\n",
      "Epoch: 14/150 Train Loss: 1.2177 Accuracy: 0.6960 Time: 7.01418  | Val Loss: 1.0914 Accuracy: 0.7600\n",
      "Epoch: 15/150 Train Loss: 1.1803 Accuracy: 0.7160 Time: 7.01119  | Val Loss: 1.0011 Accuracy: 0.7939\n",
      "Epoch: 16/150 Train Loss: 1.1678 Accuracy: 0.7215 Time: 6.99227  | Val Loss: 1.0565 Accuracy: 0.7766\n",
      "Epoch: 17/150 Train Loss: 1.1606 Accuracy: 0.7280 Time: 7.01700  | Val Loss: 1.0929 Accuracy: 0.7610\n",
      "Epoch: 18/150 Train Loss: 1.1500 Accuracy: 0.7310 Time: 7.01582  | Val Loss: 0.9703 Accuracy: 0.8076\n",
      "Epoch: 19/150 Train Loss: 1.1270 Accuracy: 0.7394 Time: 7.04625  | Val Loss: 0.9605 Accuracy: 0.8140\n",
      "Epoch: 20/150 Train Loss: 1.1289 Accuracy: 0.7366 Time: 7.03943  | Val Loss: 1.0889 Accuracy: 0.7704\n",
      "Epoch: 21/150 Train Loss: 1.0818 Accuracy: 0.7641 Time: 7.01783  | Val Loss: 0.9674 Accuracy: 0.8061\n",
      "Epoch: 22/150 Train Loss: 1.0855 Accuracy: 0.7601 Time: 7.16390  | Val Loss: 1.0322 Accuracy: 0.7865\n",
      "Epoch: 23/150 Train Loss: 1.0793 Accuracy: 0.7644 Time: 7.06123  | Val Loss: 0.9626 Accuracy: 0.8120\n",
      "Epoch: 24/150 Train Loss: 1.0570 Accuracy: 0.7709 Time: 7.00151  | Val Loss: 0.9881 Accuracy: 0.8054\n",
      "Epoch: 25/150 Train Loss: 1.0665 Accuracy: 0.7698 Time: 7.19400  | Val Loss: 0.9560 Accuracy: 0.8148\n",
      "Epoch: 26/150 Train Loss: 1.0507 Accuracy: 0.7698 Time: 7.04806  | Val Loss: 0.9807 Accuracy: 0.8036\n",
      "Epoch: 27/150 Train Loss: 1.0283 Accuracy: 0.7855 Time: 7.04480  | Val Loss: 0.8845 Accuracy: 0.8489\n",
      "Epoch: 28/150 Train Loss: 1.0222 Accuracy: 0.7860 Time: 7.03913  | Val Loss: 0.9623 Accuracy: 0.8122\n",
      "Epoch: 29/150 Train Loss: 1.0145 Accuracy: 0.7948 Time: 6.94174  | Val Loss: 1.0816 Accuracy: 0.7600\n",
      "Epoch: 30/150 Train Loss: 1.0003 Accuracy: 0.7939 Time: 6.98694  | Val Loss: 0.9045 Accuracy: 0.8400\n",
      "Epoch: 31/150 Train Loss: 0.9916 Accuracy: 0.8001 Time: 7.13725  | Val Loss: 0.8751 Accuracy: 0.8522\n",
      "Epoch: 32/150 Train Loss: 0.9854 Accuracy: 0.7988 Time: 6.99004  | Val Loss: 0.8895 Accuracy: 0.8425\n",
      "Epoch: 33/150 Train Loss: 0.9694 Accuracy: 0.8128 Time: 7.00657  | Val Loss: 0.8771 Accuracy: 0.8436\n",
      "Epoch: 34/150 Train Loss: 0.9759 Accuracy: 0.8063 Time: 6.96767  | Val Loss: 0.8821 Accuracy: 0.8461\n",
      "Epoch: 35/150 Train Loss: 0.9719 Accuracy: 0.8096 Time: 6.99092  | Val Loss: 0.8967 Accuracy: 0.8443\n",
      "Epoch: 36/150 Train Loss: 0.9494 Accuracy: 0.8168 Time: 7.02198  | Val Loss: 0.9075 Accuracy: 0.8367\n",
      "Epoch: 37/150 Train Loss: 0.9539 Accuracy: 0.8123 Time: 7.04199  | Val Loss: 0.8204 Accuracy: 0.8716\n",
      "Epoch: 38/150 Train Loss: 0.9352 Accuracy: 0.8207 Time: 7.03897  | Val Loss: 0.8259 Accuracy: 0.8693\n",
      "Epoch: 39/150 Train Loss: 0.9331 Accuracy: 0.8238 Time: 7.01589  | Val Loss: 0.8748 Accuracy: 0.8550\n",
      "Epoch: 40/150 Train Loss: 0.9286 Accuracy: 0.8263 Time: 7.02591  | Val Loss: 0.8474 Accuracy: 0.8581\n",
      "Epoch: 41/150 Train Loss: 0.9112 Accuracy: 0.8322 Time: 7.01397  | Val Loss: 0.8017 Accuracy: 0.8780\n",
      "Epoch: 42/150 Train Loss: 0.9082 Accuracy: 0.8320 Time: 7.04056  | Val Loss: 0.8548 Accuracy: 0.8550\n",
      "Epoch: 43/150 Train Loss: 0.9036 Accuracy: 0.8382 Time: 7.02470  | Val Loss: 0.8122 Accuracy: 0.8739\n",
      "Epoch: 44/150 Train Loss: 0.8915 Accuracy: 0.8383 Time: 7.06500  | Val Loss: 0.8373 Accuracy: 0.8581\n",
      "Epoch: 45/150 Train Loss: 0.8915 Accuracy: 0.8360 Time: 7.14529  | Val Loss: 0.8309 Accuracy: 0.8698\n",
      "Epoch: 46/150 Train Loss: 0.8892 Accuracy: 0.8396 Time: 7.02243  | Val Loss: 0.8021 Accuracy: 0.8759\n",
      "Epoch: 47/150 Train Loss: 0.8754 Accuracy: 0.8495 Time: 6.97546  | Val Loss: 0.8016 Accuracy: 0.8838\n",
      "Epoch: 48/150 Train Loss: 0.8767 Accuracy: 0.8456 Time: 6.91772  | Val Loss: 0.7794 Accuracy: 0.8950\n",
      "Epoch: 49/150 Train Loss: 0.8720 Accuracy: 0.8510 Time: 6.96912  | Val Loss: 0.7761 Accuracy: 0.8935\n",
      "Epoch: 50/150 Train Loss: 0.8560 Accuracy: 0.8557 Time: 6.98428  | Val Loss: 0.8065 Accuracy: 0.8803\n",
      "Epoch: 51/150 Train Loss: 0.8586 Accuracy: 0.8538 Time: 7.12146  | Val Loss: 0.8186 Accuracy: 0.8749\n",
      "Epoch: 52/150 Train Loss: 0.8525 Accuracy: 0.8554 Time: 7.02217  | Val Loss: 0.7843 Accuracy: 0.8907\n",
      "Epoch: 53/150 Train Loss: 0.8470 Accuracy: 0.8621 Time: 7.13957  | Val Loss: 0.8259 Accuracy: 0.8749\n",
      "Epoch: 54/150 Train Loss: 0.8528 Accuracy: 0.8564 Time: 7.01529  | Val Loss: 0.7751 Accuracy: 0.8940\n",
      "Epoch: 55/150 Train Loss: 0.8352 Accuracy: 0.8661 Time: 6.96089  | Val Loss: 0.7953 Accuracy: 0.8828\n",
      "Epoch: 56/150 Train Loss: 0.8265 Accuracy: 0.8638 Time: 6.98393  | Val Loss: 0.7809 Accuracy: 0.8907\n",
      "Epoch: 57/150 Train Loss: 0.8283 Accuracy: 0.8687 Time: 7.04642  | Val Loss: 0.7677 Accuracy: 0.8953\n",
      "Epoch: 58/150 Train Loss: 0.8363 Accuracy: 0.8658 Time: 7.01489  | Val Loss: 0.7634 Accuracy: 0.8978\n",
      "Epoch: 59/150 Train Loss: 0.8211 Accuracy: 0.8715 Time: 7.02606  | Val Loss: 0.7724 Accuracy: 0.8943\n",
      "Epoch: 60/150 Train Loss: 0.8089 Accuracy: 0.8772 Time: 6.99417  | Val Loss: 0.8026 Accuracy: 0.8818\n",
      "Epoch: 61/150 Train Loss: 0.8148 Accuracy: 0.8730 Time: 6.97655  | Val Loss: 0.7825 Accuracy: 0.8907\n",
      "Epoch: 62/150 Train Loss: 0.7980 Accuracy: 0.8806 Time: 7.00603  | Val Loss: 0.7467 Accuracy: 0.9057\n",
      "Epoch: 63/150 Train Loss: 0.8028 Accuracy: 0.8772 Time: 7.04320  | Val Loss: 0.7662 Accuracy: 0.8910\n",
      "Epoch: 64/150 Train Loss: 0.7942 Accuracy: 0.8825 Time: 7.11805  | Val Loss: 0.7693 Accuracy: 0.8950\n",
      "Epoch: 65/150 Train Loss: 0.7941 Accuracy: 0.8817 Time: 7.02248  | Val Loss: 0.7579 Accuracy: 0.9001\n",
      "Epoch: 66/150 Train Loss: 0.7934 Accuracy: 0.8825 Time: 6.99039  | Val Loss: 0.7737 Accuracy: 0.8927\n",
      "Epoch: 67/150 Train Loss: 0.7887 Accuracy: 0.8837 Time: 6.98090  | Val Loss: 0.7593 Accuracy: 0.8999\n",
      "Epoch: 68/150 Train Loss: 0.7766 Accuracy: 0.8897 Time: 6.97783  | Val Loss: 0.7586 Accuracy: 0.8981\n",
      "Epoch: 69/150 Train Loss: 0.7715 Accuracy: 0.8923 Time: 7.01557  | Val Loss: 0.7791 Accuracy: 0.8866\n",
      "Epoch: 70/150 Train Loss: 0.7710 Accuracy: 0.8920 Time: 6.99366  | Val Loss: 0.7634 Accuracy: 0.8999\n",
      "Epoch: 71/150 Train Loss: 0.7703 Accuracy: 0.8933 Time: 7.02543  | Val Loss: 0.7416 Accuracy: 0.9057\n",
      "Epoch: 72/150 Train Loss: 0.7671 Accuracy: 0.8919 Time: 6.98694  | Val Loss: 0.7408 Accuracy: 0.9062\n",
      "Epoch: 73/150 Train Loss: 0.7691 Accuracy: 0.8932 Time: 7.06104  | Val Loss: 0.7317 Accuracy: 0.9090\n",
      "Epoch: 74/150 Train Loss: 0.7609 Accuracy: 0.8973 Time: 6.96177  | Val Loss: 0.7333 Accuracy: 0.9093\n",
      "Epoch: 75/150 Train Loss: 0.7462 Accuracy: 0.9025 Time: 7.01599  | Val Loss: 0.7413 Accuracy: 0.9057\n",
      "Epoch: 76/150 Train Loss: 0.7450 Accuracy: 0.9045 Time: 7.12976  | Val Loss: 0.7383 Accuracy: 0.9055\n",
      "Epoch: 77/150 Train Loss: 0.7465 Accuracy: 0.9013 Time: 6.99369  | Val Loss: 0.7344 Accuracy: 0.9093\n",
      "Epoch: 78/150 Train Loss: 0.7328 Accuracy: 0.9084 Time: 6.95187  | Val Loss: 0.7326 Accuracy: 0.9080\n",
      "Epoch: 79/150 Train Loss: 0.7369 Accuracy: 0.9031 Time: 7.04540  | Val Loss: 0.7431 Accuracy: 0.9045\n",
      "Epoch: 80/150 Train Loss: 0.7361 Accuracy: 0.9082 Time: 6.98238  | Val Loss: 0.7302 Accuracy: 0.9093\n",
      "Epoch: 81/150 Train Loss: 0.7318 Accuracy: 0.9078 Time: 7.01416  | Val Loss: 0.7416 Accuracy: 0.9075\n",
      "Epoch: 82/150 Train Loss: 0.7275 Accuracy: 0.9101 Time: 6.99787  | Val Loss: 0.7397 Accuracy: 0.9024\n",
      "Epoch: 83/150 Train Loss: 0.7294 Accuracy: 0.9092 Time: 6.99871  | Val Loss: 0.7282 Accuracy: 0.9152\n",
      "Epoch: 84/150 Train Loss: 0.7239 Accuracy: 0.9095 Time: 7.16313  | Val Loss: 0.7177 Accuracy: 0.9159\n",
      "Epoch: 85/150 Train Loss: 0.7113 Accuracy: 0.9151 Time: 7.01910  | Val Loss: 0.7207 Accuracy: 0.9144\n",
      "Epoch: 86/150 Train Loss: 0.7091 Accuracy: 0.9183 Time: 7.03023  | Val Loss: 0.7155 Accuracy: 0.9162\n",
      "Epoch: 87/150 Train Loss: 0.7032 Accuracy: 0.9196 Time: 7.18347  | Val Loss: 0.7304 Accuracy: 0.9103\n",
      "Epoch: 88/150 Train Loss: 0.7096 Accuracy: 0.9175 Time: 6.98383  | Val Loss: 0.7231 Accuracy: 0.9116\n",
      "Epoch: 89/150 Train Loss: 0.7071 Accuracy: 0.9176 Time: 7.10004  | Val Loss: 0.7225 Accuracy: 0.9113\n",
      "Epoch: 90/150 Train Loss: 0.6961 Accuracy: 0.9261 Time: 7.07156  | Val Loss: 0.7109 Accuracy: 0.9192\n",
      "Epoch: 91/150 Train Loss: 0.6875 Accuracy: 0.9277 Time: 6.98099  | Val Loss: 0.7370 Accuracy: 0.9111\n",
      "Epoch: 92/150 Train Loss: 0.7059 Accuracy: 0.9168 Time: 7.02006  | Val Loss: 0.7174 Accuracy: 0.9146\n",
      "Epoch: 93/150 Train Loss: 0.7004 Accuracy: 0.9205 Time: 6.96791  | Val Loss: 0.7284 Accuracy: 0.9146\n",
      "Epoch: 94/150 Train Loss: 0.6805 Accuracy: 0.9326 Time: 6.97406  | Val Loss: 0.7108 Accuracy: 0.9203\n",
      "Epoch: 95/150 Train Loss: 0.6850 Accuracy: 0.9269 Time: 6.99058  | Val Loss: 0.7114 Accuracy: 0.9157\n",
      "Epoch: 96/150 Train Loss: 0.6846 Accuracy: 0.9285 Time: 6.96491  | Val Loss: 0.7099 Accuracy: 0.9195\n",
      "Epoch: 97/150 Train Loss: 0.6773 Accuracy: 0.9320 Time: 6.99169  | Val Loss: 0.7083 Accuracy: 0.9182\n",
      "Epoch: 98/150 Train Loss: 0.6814 Accuracy: 0.9300 Time: 7.02890  | Val Loss: 0.7137 Accuracy: 0.9203\n",
      "Epoch: 99/150 Train Loss: 0.6685 Accuracy: 0.9358 Time: 6.96845  | Val Loss: 0.7093 Accuracy: 0.9208\n",
      "Epoch: 100/150 Train Loss: 0.6737 Accuracy: 0.9324 Time: 6.94297  | Val Loss: 0.7069 Accuracy: 0.9208\n",
      "Epoch: 101/150 Train Loss: 0.6688 Accuracy: 0.9346 Time: 7.07680  | Val Loss: 0.6975 Accuracy: 0.9218\n",
      "Epoch: 102/150 Train Loss: 0.6737 Accuracy: 0.9332 Time: 6.94063  | Val Loss: 0.7049 Accuracy: 0.9215\n",
      "Epoch: 103/150 Train Loss: 0.6811 Accuracy: 0.9307 Time: 7.06356  | Val Loss: 0.7009 Accuracy: 0.9172\n",
      "Epoch: 104/150 Train Loss: 0.6654 Accuracy: 0.9359 Time: 7.04017  | Val Loss: 0.6965 Accuracy: 0.9223\n",
      "Epoch: 105/150 Train Loss: 0.6574 Accuracy: 0.9387 Time: 7.04551  | Val Loss: 0.6984 Accuracy: 0.9215\n",
      "Epoch: 106/150 Train Loss: 0.6593 Accuracy: 0.9381 Time: 6.95558  | Val Loss: 0.7021 Accuracy: 0.9223\n",
      "Epoch: 107/150 Train Loss: 0.6556 Accuracy: 0.9400 Time: 6.97795  | Val Loss: 0.6962 Accuracy: 0.9248\n",
      "Epoch: 108/150 Train Loss: 0.6530 Accuracy: 0.9406 Time: 7.06037  | Val Loss: 0.7043 Accuracy: 0.9218\n",
      "Epoch: 109/150 Train Loss: 0.6570 Accuracy: 0.9400 Time: 6.97022  | Val Loss: 0.6997 Accuracy: 0.9251\n",
      "Epoch: 110/150 Train Loss: 0.6515 Accuracy: 0.9409 Time: 7.05423  | Val Loss: 0.6987 Accuracy: 0.9248\n",
      "Epoch: 111/150 Train Loss: 0.6579 Accuracy: 0.9400 Time: 6.99982  | Val Loss: 0.7008 Accuracy: 0.9220\n",
      "Epoch: 112/150 Train Loss: 0.6561 Accuracy: 0.9390 Time: 7.04063  | Val Loss: 0.7039 Accuracy: 0.9203\n",
      "Epoch: 113/150 Train Loss: 0.6457 Accuracy: 0.9457 Time: 7.01276  | Val Loss: 0.6967 Accuracy: 0.9223\n",
      "Epoch: 114/150 Train Loss: 0.6497 Accuracy: 0.9421 Time: 6.97892  | Val Loss: 0.7026 Accuracy: 0.9205\n",
      "Epoch: 115/150 Train Loss: 0.6420 Accuracy: 0.9471 Time: 7.04200  | Val Loss: 0.6966 Accuracy: 0.9243\n",
      "Epoch: 116/150 Train Loss: 0.6409 Accuracy: 0.9467 Time: 7.03378  | Val Loss: 0.6952 Accuracy: 0.9238\n",
      "Epoch: 117/150 Train Loss: 0.6479 Accuracy: 0.9439 Time: 7.05049  | Val Loss: 0.6934 Accuracy: 0.9236\n",
      "Epoch: 118/150 Train Loss: 0.6456 Accuracy: 0.9448 Time: 6.97219  | Val Loss: 0.7016 Accuracy: 0.9218\n",
      "Epoch: 119/150 Train Loss: 0.6450 Accuracy: 0.9449 Time: 7.04047  | Val Loss: 0.6974 Accuracy: 0.9248\n",
      "Epoch: 120/150 Train Loss: 0.6439 Accuracy: 0.9446 Time: 7.01665  | Val Loss: 0.6943 Accuracy: 0.9220\n",
      "Epoch: 121/150 Train Loss: 0.6361 Accuracy: 0.9475 Time: 6.97641  | Val Loss: 0.6936 Accuracy: 0.9266\n",
      "Epoch: 122/150 Train Loss: 0.6349 Accuracy: 0.9489 Time: 7.08379  | Val Loss: 0.6956 Accuracy: 0.9243\n",
      "Epoch: 123/150 Train Loss: 0.6323 Accuracy: 0.9498 Time: 6.99762  | Val Loss: 0.6908 Accuracy: 0.9264\n",
      "Epoch: 124/150 Train Loss: 0.6332 Accuracy: 0.9486 Time: 7.05882  | Val Loss: 0.6925 Accuracy: 0.9246\n",
      "Epoch: 125/150 Train Loss: 0.6327 Accuracy: 0.9500 Time: 7.03539  | Val Loss: 0.6910 Accuracy: 0.9287\n",
      "Epoch: 126/150 Train Loss: 0.6275 Accuracy: 0.9514 Time: 7.03073  | Val Loss: 0.6900 Accuracy: 0.9274\n",
      "Epoch: 127/150 Train Loss: 0.6362 Accuracy: 0.9464 Time: 7.00921  | Val Loss: 0.6930 Accuracy: 0.9259\n",
      "Epoch: 128/150 Train Loss: 0.6326 Accuracy: 0.9475 Time: 6.98960  | Val Loss: 0.6907 Accuracy: 0.9266\n",
      "Epoch: 129/150 Train Loss: 0.6267 Accuracy: 0.9507 Time: 7.09971  | Val Loss: 0.6876 Accuracy: 0.9276\n",
      "Epoch: 130/150 Train Loss: 0.6270 Accuracy: 0.9513 Time: 7.02536  | Val Loss: 0.6895 Accuracy: 0.9269\n",
      "Epoch: 131/150 Train Loss: 0.6264 Accuracy: 0.9527 Time: 6.97205  | Val Loss: 0.6885 Accuracy: 0.9259\n",
      "Epoch: 132/150 Train Loss: 0.6186 Accuracy: 0.9553 Time: 7.00320  | Val Loss: 0.6873 Accuracy: 0.9284\n",
      "Epoch: 133/150 Train Loss: 0.6261 Accuracy: 0.9508 Time: 7.13502  | Val Loss: 0.6878 Accuracy: 0.9276\n",
      "Epoch: 134/150 Train Loss: 0.6213 Accuracy: 0.9526 Time: 7.00158  | Val Loss: 0.6853 Accuracy: 0.9269\n",
      "Epoch: 135/150 Train Loss: 0.6195 Accuracy: 0.9563 Time: 6.97547  | Val Loss: 0.6837 Accuracy: 0.9289\n",
      "Epoch: 136/150 Train Loss: 0.6290 Accuracy: 0.9491 Time: 7.12357  | Val Loss: 0.6846 Accuracy: 0.9269\n",
      "Epoch: 137/150 Train Loss: 0.6212 Accuracy: 0.9554 Time: 7.00240  | Val Loss: 0.6853 Accuracy: 0.9284\n",
      "Epoch: 138/150 Train Loss: 0.6258 Accuracy: 0.9522 Time: 6.99168  | Val Loss: 0.6835 Accuracy: 0.9282\n",
      "Epoch: 139/150 Train Loss: 0.6249 Accuracy: 0.9531 Time: 7.03355  | Val Loss: 0.6851 Accuracy: 0.9266\n",
      "Epoch: 140/150 Train Loss: 0.6167 Accuracy: 0.9570 Time: 7.06824  | Val Loss: 0.6847 Accuracy: 0.9276\n",
      "Epoch: 141/150 Train Loss: 0.6225 Accuracy: 0.9519 Time: 6.94026  | Val Loss: 0.6841 Accuracy: 0.9279\n",
      "Epoch: 142/150 Train Loss: 0.6231 Accuracy: 0.9540 Time: 7.00783  | Val Loss: 0.6830 Accuracy: 0.9299\n",
      "Epoch: 143/150 Train Loss: 0.6188 Accuracy: 0.9554 Time: 7.04947  | Val Loss: 0.6832 Accuracy: 0.9297\n",
      "Epoch: 144/150 Train Loss: 0.6275 Accuracy: 0.9515 Time: 6.98068  | Val Loss: 0.6842 Accuracy: 0.9269\n",
      "Epoch: 145/150 Train Loss: 0.6203 Accuracy: 0.9551 Time: 7.01311  | Val Loss: 0.6847 Accuracy: 0.9276\n",
      "Epoch: 146/150 Train Loss: 0.6200 Accuracy: 0.9540 Time: 7.01949  | Val Loss: 0.6832 Accuracy: 0.9284\n",
      "Epoch: 147/150 Train Loss: 0.6206 Accuracy: 0.9535 Time: 6.99211  | Val Loss: 0.6838 Accuracy: 0.9276\n",
      "Epoch: 148/150 Train Loss: 0.6133 Accuracy: 0.9570 Time: 6.96434  | Val Loss: 0.6847 Accuracy: 0.9276\n",
      "Epoch: 149/150 Train Loss: 0.6212 Accuracy: 0.9541 Time: 7.00698  | Val Loss: 0.6837 Accuracy: 0.9276\n",
      "Epoch: 150/150 Train Loss: 0.6248 Accuracy: 0.9525 Time: 6.98679  | Val Loss: 0.6837 Accuracy: 0.9282\n"
     ]
    }
   ],
   "source": [
    "from hdd.data_util.auto_augmentation import ImageNetPolicy\n",
    "\n",
    "from hdd.data_util.transforms import RandomResize\n",
    "from torch.utils.data import DataLoader\n",
    "from hdd.models.cnn.mobilenet_v3 import create_mobilenetV3\n",
    "from hdd.train.classification_utils import (\n",
    "    naive_train_classification_model,\n",
    ")\n",
    "from hdd.models.nn_utils import count_trainable_parameter\n",
    "\n",
    "\n",
    "def train_net(\n",
    "    net,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    lr=1e-3,\n",
    "    weight_decay=1e-3,\n",
    "    max_epochs=100,\n",
    ") -> dict[str, list[float]]:\n",
    "    print(f\"#Parameter: {count_trainable_parameter(net)}\")\n",
    "    criteria = nn.CrossEntropyLoss(label_smoothing=0.1)\n",
    "    optimizer = torch.optim.AdamW(net.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
    "        optimizer, max_epochs, eta_min=lr / 100\n",
    "    )\n",
    "    training_stats = naive_train_classification_model(\n",
    "        net,\n",
    "        criteria,\n",
    "        max_epochs,\n",
    "        train_dataloader,\n",
    "        val_dataloader,\n",
    "        DEVICE,\n",
    "        optimizer,\n",
    "        scheduler,\n",
    "        verbose=True,\n",
    "    )\n",
    "    return training_stats\n",
    "\n",
    "\n",
    "def build_dataloader(batch_size, train_dataset, val_dataset):\n",
    "    train_dataloader = DataLoader(\n",
    "        train_dataset, batch_size=batch_size, shuffle=True, num_workers=8\n",
    "    )\n",
    "    val_dataloader = DataLoader(\n",
    "        val_dataset, batch_size=batch_size, shuffle=False, num_workers=8\n",
    "    )\n",
    "    return train_dataloader, val_dataloader\n",
    "\n",
    "\n",
    "# 我们提前计算好了训练数据集上的均值和方差\n",
    "TRAIN_MEAN = [0.4625, 0.4580, 0.4295]\n",
    "TRAIN_STD = [0.2452, 0.2390, 0.2469]\n",
    "\n",
    "stats = {}\n",
    "net_names = [\"mobilenet_v3_small\", \"mobilenet_v3_large\"]\n",
    "batch_sizes = [64, 32]\n",
    "for net_name, batch_size in zip(net_names, batch_sizes):\n",
    "    net = create_mobilenetV3(net_name, 1, 10, 0.5)\n",
    "    net = net.to(DEVICE)\n",
    "    train_dataset_transforms = transforms.Compose(\n",
    "        [\n",
    "            RandomResize([256, 288, 320, 352]),\n",
    "            transforms.RandomCrop(224),\n",
    "            transforms.RandomHorizontalFlip(),\n",
    "            ImageNetPolicy(),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "        ]\n",
    "    )\n",
    "    train_dataset.transform = train_dataset_transforms\n",
    "    val_dataset_transforms = transforms.Compose(\n",
    "        [\n",
    "            transforms.Resize(256),\n",
    "            transforms.CenterCrop(224),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "        ]\n",
    "    )\n",
    "    val_dataset.transform = val_dataset_transforms\n",
    "    train_dataloader, val_dataloader = build_dataloader(\n",
    "        batch_size, train_dataset, val_dataset\n",
    "    )\n",
    "    print(f\"-----------------------{net_name}------------------------\")\n",
    "    max_epochs = 150\n",
    "    stats[net_name] = train_net(\n",
    "        net,\n",
    "        train_dataloader,\n",
    "        val_dataloader,\n",
    "        lr=0.001,\n",
    "        weight_decay=0,\n",
    "        max_epochs=max_epochs,\n",
    "    )\n",
    "    del net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0bd493ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "fields = stats[net_names[0]].keys()\n",
    "for i, field in enumerate(fields):\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    plt.plot(stats[net_names[0]][field], label=net_names[0], linestyle=\"--\")\n",
    "    plt.plot(stats[net_names[1]][field], label=net_names[1])\n",
    "    plt.legend()\n",
    "    plt.title(field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47e4448a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-cu124",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
